Using Artificial Intelligence for Pattern Recognition in a Sports Context

Abstract

Optimizing athlete’s performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.

[Retracted] Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

“…To elaborate, one merely needs a mobile phone or a few-square-meter field to do AI sports. As far as badminton is concerned, the user just needs to open the power app, position the phone on the side of the playing field in an appropriate angle, and adjust the phone-subject distance according to the app's automatic voice prompts until the entire image of the player is incorporated in the recognition frame [6].…”

Using Optical Tracking System Data to Measure Team Synergic Behavior: Synchronization of Player-Ball-Goal Angles in a Football Match

“…The high rate and high resolution of this type of kinematic data enables the creation of large datasets which can capture subtle changes in performance [ 1 ]. When collected over time, these data can feed modern tools of pattern behavior classification, such as machine learning [ 2 ]. For this, collecting longitudinal data from matches and leagues is a logical next step.…”

“…The use of electronic performance-tracking systems to obtain spatiotemporal data in association football (football, for simplicity) is becoming a generalized practice for performance analysis. These systems include global positioning systems, radio-based local positioning systems and video camera-based systems, which provide big data about the individual performances of players and tactical behaviors of teams [ 1 , 2 , 3 ].…”

Application of Artificial Intelligence in Basketball Sport

“…AI-driven vision processing technology can accurately track the ball to the millimeter. This technique has been used by referees in many sports, such as cricket, tennis, football, baseball, and billiards [2].…”